Pre-symptomatic detection of Alzheimer's disease and mild cognitive impairment
Bibliographic record
Abstract
The clinical diagnosis of Alzheimer's disease (AD) is occasionally imprecise using consensus criteria for probable AD. Therefore, there is a great need for simple biomarkers that substantially aid early diagnosis and tract disease progression of AD and mild cognitive impairment. Of currently available biomarkers for AD, imaging markers are of particular importance based on their low invasiveness and reproducibility. In vivo detection of brain amyloid burden using positron emission tomography either by PIB or BF-227 would be quite attractive. In Japan, Alzheimer's disease neuroimaging initiatives (ADNI) has been launched in 2008 in accordance with US- and World-Wide ADNI. The paradigm of AD diagnosis and treatment would be shifted from "cognitive-based" to "biomarker-based" framework. The use of ideal biomarkers can remarkably speed up AD drug discovery by providing earliest signals of drug efficacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".